PROJECT SUMMARY This project will address the critical need for new and effective antitubercular drugs. Our primary objective is to elucidate the mechanisms by which Mycobacterium tuberculosis tolerates antitubercular drug treatment. Our motivating hypothesis is that M. tuberculosis tolerates drug induced stress by differentially regulating detoxification enzymes, efflux pumps, metabolic activity, pellicle-forming factors, and cell wall remodeling systems. Further, we postulate that a secondary drug targeting one or few regulators of these tolerance strategies will potentiate the primary drug-treatment, and potentially reduce the emergence of resistance. We propose a systems biology approach to generate a network perspective of drug-induced tolerance mechanisms and how they are coordinated by one or few regulators that could be targeted for overcoming drug-specific tolerance using combinatorial treatment regimens. Hence, the innovation of our proposed research emerges from integrating network characterization of drug- specific tolerance mechanisms into the rational discovery of novel drug combinations. In Aim 1, we will transcriptionally profile M. tuberculosis following treatment with ten selected drugs (primary drugs). Using techniques developed in our laboratory, differentially expressed genes will be mapped onto a systems-scale gene regulatory network model of M. tuberculosis to infer drug-specific tolerance sub-networks and elucidate key regulators. We will also identify tolerance sub-networks by generating genome-wide fitness profiles in the presence of the selected primary drugs. Drug-associated fitness defects will reveal genes that are important for dealing with drug-induced stress and are hypothesized to cluster together in drug-specific tolerance sub-networks. In Aim 2, we will transcriptionally profile ~250 secondary drugs and perform combination high-throughput screens of all primary and secondary drug combinations. Data from these studies will be used to iteratively refine the model and develop a machine learning algorithm to identify gene- and network-level features that are predictive of synergistic drug interactions. Finally, mechanism of synergistic drug combinations will be characterized by selectively perturbing the predicted regulators of the tolerance sub-networks. This project will propel the development of systems biology tools to accurately predict novel synergistic drug combinations, thereby guiding experimental assessment and accelerating the delivery of new treatments to patients with tuberculosis infection.